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Developing a Naive Bayes Text Classifier in JAVA

In previous articles we have discussed the theoretical background of Naive Bayes Text Classifier and the importance of using Feature Selection techniques in Text Classification. In this article, we are going to put everything together and build a simple implementation of the Naive Bayes text classification algorithm in JAVA. The code of the classifier is […]

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NaiveBayes-JAVAIn previous articles we have discussed the theoretical background of Naive Bayes Text Classifier and the importance of using Feature Selection techniques in Text Classification. In this article, we are going to put everything together and build a simple implementation of the Naive Bayes text classification algorithm in JAVA. The code of the classifier is open-sourced (under GPL v3 license) and you can download it from Github.

Update: The Datumbox Machine Learning Framework is now open-source and free to download. Check out the package com.datumbox.framework.machinelearning.classification to see the implementation of Naive Bayes Classifier in Java.

Naive Bayes Java Implementation

The code is written in JAVA and can be downloaded directly from Github. It is licensed under GPLv3 so feel free to use it, modify it and redistribute it freely.

The Text Classifier implements the Multinomial Naive Bayes model along with the Chisquare Feature Selection algorithm. All the theoretical details of how both techniques work are covered in previous articles and detailed javadoc comments can be found on the source code describing the implementation. Thus in this segment I will focus on a high level description of the architecture of the classifier.

1. NaiveBayes Class

This is the main part of the Text Classifier. It implements methods such as train() and predict() which are responsible for training a classifier and using it for predictions. It should be noted that this class is also responsible for calling the appropriate external methods to preprocess and tokenize the document before training/prediction.

2. NaiveBayesKnowledgeBase Object

The output of training is a NaiveBayesKnowledgeBase Object which stores all the necessary information and probabilities that are used by the Naive Bayes Classifier.

3. Document Object

Both the training and the prediction texts in the implementation are internally stored as Document Objects. The Document Object stores all the tokens (words) of the document, their statistics and the target classification of the document.

4. FeatureStats Object

The FeatureStats Object stores several statistics that are generated during the Feature Extraction phase. Such statistics are the Joint counts of Features and Class (from which the joint probabilities and likelihoods are estimated), the Class counts (from which the priors are evaluated if none are given as input) and the total number of observations used for training.

5. FeatureExtraction Class

This is the class which is responsible for performing feature extraction. It should be noted that since this class calculates internally several of the statistics that are actually required by the classification algorithm in the later stage, all these stats are cached and returned in a FeatureStats Object to avoid their recalculation.

6. TextTokenizer Class

This is a simple text tokenization class, responsible for preprocessing, clearing and tokenizing the original texts and converting them into Document objects.

Using the NaiveBayes JAVA Class

In the NaiveBayesExample class you can find examples of using the NaiveBayes Class. The target of the sample code is to present an example which trains a simple Naive Bayes Classifier in order to detect the Language of a text. To train the classifier, initially we provide the paths of the training datasets in a HashMap and then we load their contents.

 //map of dataset files Map<String, URL> trainingFiles = new HashMap<>(); trainingFiles.put("English", NaiveBayesExample.class.getResource("/datasets/training.language.en.txt")); trainingFiles.put("French", NaiveBayesExample.class.getResource("/datasets/training.language.fr.txt")); trainingFiles.put("German", NaiveBayesExample.class.getResource("/datasets/training.language.de.txt")); //loading examples in memory Map<String, String[]> trainingExamples = new HashMap<>(); for(Map.Entry<String, URL> entry : trainingFiles.entrySet()) { trainingExamples.put(entry.getKey(), readLines(entry.getValue())); }

The NaiveBayes classifier is trained by passing to it the data. Once the training is completed the NaiveBayesKnowledgeBase Object is stored for later use.

 //train classifier NaiveBayes nb = new NaiveBayes(); nb.setChisquareCriticalValue(6.63); //0.01 pvalue nb.train(trainingExamples); //get trained classifier NaiveBayesKnowledgeBase knowledgeBase = nb.getKnowledgeBase();

Finally to use the classifier and predict the classes of new examples all you need to do is initialize a new classifier by passing the NaiveBayesKnowledgeBase Object which you acquired earlier by training. Then by calling simply the predict() method you get the predicted class of the document.

 //Test classifier nb = new NaiveBayes(knowledgeBase); String exampleEn = "I am English"; String outputEn = nb.predict(exampleEn); System.out.format("The sentense "%s" was classified as "%s".%n", exampleEn, outputEn); 

Necessary Expansions

The particular JAVA implementation should not be considered a complete ready to use solution for sophisticated text classification problems. Here are some of the important expansions that could be done:

1. Keyword Extraction:

Even though using single keywords can be sufficient for simple problems such as Language Detection, other more complicated problems require the extraction of n-grams. Thus one can either implement a more sophisticated text extraction algorithm by updating the TextTokenizer.extractKeywords() method or use Datumbox’s Keyword Extraction API function to get all the n-grams (keyword combinations) of the document.

2. Text Preprocessing:

Before using a classifier usually it is necessary to preprocess the document in order to remove unnecessary characters/parts. Even though the current implementation performs limited preprocessing by using the TextTokenizer.preprocess() method, when it comes to analyzing HTML pages things become trickier. One can simply trim out the HTML tags and keep only the plain text of the document or resort to more sophisticate Machine Learning techniques that detect the main text of the page and remove content which belongs to footer, headers, menus etc. For the later you can use Datumbox’s Text Extraction API function.

3. Additional Naive Bayes Models:

The current classifier implements the Multinomial Naive Bayes classifier, nevertheless as we discussed in a previous article about Sentiment Analysis, different classification problems require different models. In some a Binarized version of the algorithm would be more appropriate, while in others the Bernoulli Model will provide much better results. Use this implementation as a starting point and follow the instructions of the Naive Bayes Tutorial to expand the model.

4. Additional Feature Selection Methods:

This implementation uses the Chisquare feature selection algorithm to select the most appropriate features for the classification. As we saw in a previous article, the Chisquare feature selection method is a good technique which relays on statistics to select the appropriate features, nevertheless it tends to give higher scores on rare features that only appear in one of the categories. Improvements can be made removing noisy/rare features before proceeding to feature selection or by implementing additional methods such as the Mutual Information that we discussed on the aforementioned article.

5. Performance Optimization:

In the particular implementation it was important to improve the readability of the code rather than performing micro-optimizations on the code. Despite the fact that such optimizations make the code uglier and harder to read/maintain, they are often necessary since many loops in this algorithm are executed millions of times during training and testing. This implementation can be a great starting point for developing your own tuned version.

Almost there… Final Notes!

I-heard-hes-good-at-coding-lTo get a good understanding of how this implementation works you are strongly advised to read the two previous articles about Naive Bayes Classifier and Feature Selection. You will get insights on the theoretical background of the methods and it will make parts of the algorithm/code clearer.

We should note that Naive Bayes despite being an easy, fast and most of the times “quite accurate”, it is also “Naive” because it makes the assumption of conditional independence of the features. Since this assumption is almost never met in Text Classification problems, the Naive Bayes is almost never the best performing classifier. In Datumbox API, some expansions of the standard Naive Bayes classifier are used only for simple problems such as Language Detection. For more complicated text classification problems more advanced techniques such as the Max Entropy classifier are necessary.

If you use the implementation in an interesting project drop us a line and we will feature your project on our blog. Also if you like the article please take a moment and share it on Twitter or Facebook. 🙂

About Vasilis Vryniotis

My name is Vasilis Vryniotis. I’m a Data Scientist, a Software Engineer, author of Datumbox Machine Learning Framework and a proud geek. Learn more

Source: http://blog.datumbox.com/developing-a-naive-bayes-text-classifier-in-java/

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Graph Convolutional Networks (GCN)

In this post, we’re gonna take a close look at one of the well-known graph neural networks named Graph Convolutional Network (GCN). First, we’ll get the intuition to see how it works, then we’ll go deeper into the maths behind it. Why Graphs? Many problems are graphs in true nature. In our world, we see many data are graphs, […]

The post Graph Convolutional Networks (GCN) appeared first on TOPBOTS.

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graph convolutional networks

In this post, we’re gonna take a close look at one of the well-known graph neural networks named Graph Convolutional Network (GCN). First, we’ll get the intuition to see how it works, then we’ll go deeper into the maths behind it.

Why Graphs?

Many problems are graphs in true nature. In our world, we see many data are graphs, such as molecules, social networks, and paper citations networks.

Tasks on Graphs

  • Node classification: Predict a type of a given node
  • Link prediction: Predict whether two nodes are linked
  • Community detection: Identify densely linked clusters of nodes
  • Network similarity: How similar are two (sub)networks

Machine Learning Lifecycle

In the graph, we have node features (the data of nodes) and the structure of the graph (how nodes are connected).

For the former, we can easily get the data from each node. But when it comes to the structure, it is not trivial to extract useful information from it. For example, if 2 nodes are close to one another, should we treat them differently to other pairs? How about high and low degree nodes? In fact, each specific task can consume a lot of time and effort just for Feature Engineering, i.e., to distill the structure into our features.

graph convolutional network
Feature engineering on graphs. (Picture from [1])

It would be much better to somehow get both the node features and the structure as the input, and let the machine to figure out what information is useful by itself.

That’s why we need Graph Representation Learning.

graph convolutional network
We want the graph can learn the “feature engineering” by itself. (Picture from [1])

If this in-depth educational content on convolutional neural networks is useful for you, you can subscribe to our AI research mailing list to be alerted when we release new material. 

Graph Convolutional Networks (GCNs)

Paper: Semi-supervised Classification with Graph Convolutional Networks (2017) [3]

GCN is a type of convolutional neural network that can work directly on graphs and take advantage of their structural information.

it solves the problem of classifying nodes (such as documents) in a graph (such as a citation network), where labels are only available for a small subset of nodes (semi-supervised learning).

graph convolutional network
Example of Semi-supervised learning on Graphs. Some nodes dont have labels (unknown nodes).

Main Ideas

As the name “Convolutional” suggests, the idea was from Images and then brought to Graphs. However, when Images have a fixed structure, Graphs are much more complex.

graph convolutional network
Convolution idea from images to graphs. (Picture from [1])

The general idea of GCN: For each node, we get the feature information from all its neighbors and of course, the feature of itself. Assume we use the average() function. We will do the same for all the nodes. Finally, we feed these average values into a neural network.

In the following figure, we have a simple example with a citation network. Each node represents a research paper, while edges are the citations. We have a pre-process step here. Instead of using the raw papers as features, we convert the papers into vectors (by using NLP embedding, e.g., tf–idf).

Let’s consider the green node. First off, we get all the feature values of its neighbors, including itself, then take the average. The result will be passed through a neural network to return a resulting vector.

graph convolutional network
The main idea of GCN. Consider the green node. First, we take the average of all its neighbors, including itself. After that, the average value is passed through a neural network. Note that, in GCN, we simply use a fully connected layer. In this example, we get 2-dimension vectors as the output (2 nodes at the fully connected layer).

In practice, we can use more sophisticated aggregate functions rather than the average function. We can also stack more layers on top of each other to get a deeper GCN. The output of a layer will be treated as the input for the next layer.

graph convolutional network
Example of 2-layer GCN: The output of the first layer is the input of the second layer. Again, note that the neural network in GCN is simply a fully connected layer (Picture from [2])

Let’s take a closer look at the maths to see how it really works.

Intuition and the Maths behind

First, we need some notations

Let’s consider a graph G as below.

graph convolutional network
From the graph G, we have an adjacency matrix A and a Degree matrix D. We also have feature matrix X.

How can we get all the feature values from neighbors for each node? The solution lies in the multiplication of A and X.

Take a look at the first row of the adjacency matrix, we see that node A has a connection to E. The first row of the resulting matrix is the feature vector of E, which A connects to (Figure below). Similarly, the second row of the resulting matrix is the sum of feature vectors of D and E. By doing this, we can get the sum of all neighbors’ vectors.

graph convolutional network
Calculate the first row of the “sum vector matrix” AX
  • There are still some things that need to improve here.
  1. We miss the feature of the node itself. For example, the first row of the result matrix should contain features of node A too.
  2. Instead of sum() function, we need to take the average, or even better, the weighted average of neighbors’ feature vectors. Why don’t we use the sum() function? The reason is that when using the sum() function, high-degree nodes are likely to have huge v vectors, while low-degree nodes tend to get small aggregate vectors, which may later cause exploding or vanishing gradients (e.g., when using sigmoid). Besides, Neural networks seem to be sensitive to the scale of input data. Thus, we need to normalize these vectors to get rid of the potential issues.

In Problem (1), we can fix by adding an Identity matrix I to A to get a new adjacency matrix Ã.

Pick lambda = 1 (the feature of the node itself is just important as its neighbors), we have Ã = A + I. Note that we can treat lambda as a trainable parameter, but for now, just assign the lambda to 1, and even in the paper, lambda is just simply assigned to 1.

By adding a self-loop to each node, we have the new adjacency matrix

Problem (2)For matrix scaling, we usually multiply the matrix by a diagonal matrix. In this case, we want to take the average of the sum feature, or mathematically, to scale the sum vector matrix ÃX according to the node degrees. The gut feeling tells us that our diagonal matrix used to scale here is something related to the Degree matrix D̃ (Why , not D? Because we’re considering Degree matrix  of new adjacency matrix Ã, not A anymore).

The problem now becomes how we want to scale/normalize the sum vectors? In other words:

How we pass the information from neighbors to a specific node?

We would start with our old friend average. In this case, D̃ inverse (i.e., D̃^{-1}) comes into play. Basically, each element in D̃ inverse is the reciprocal of its corresponding term of the diagonal matrix D.

For example, node A has a degree of 2, so we multiple the sum vectors of node A by 1/2, while node E has a degree of 5, we should multiple the sum vector of E by 1/5, and so on.

Thus, by taking the multiplication of D̃ inverse and X, we can take the average of all neighbors’ feature vectors (including itself).

So far so good. But you may ask How about the weighted average()?. Intuitively, it should be better if we treat high and low degree nodes differently.

We’re just scaling by rows, but ignoring their corresponding columns (dash boxes)
Add a new scaler for columns.

The new scaler gives us the “weighted” average. What are we doing here is to put more weights on the nodes that have low-degree and reduce the impact of high-degree nodes. The idea of this weighted average is that we assume low-degree nodes would have bigger impacts on their neighbors, whereas high-degree nodes generate lower impacts as they scatter their influence at too many neighbors.

graph convolutional network
When aggregating feature at node B, we assign the biggest weight for node B itself (degree of 3), and the lowest weight for node E (degree of 5)
Because we normalize twice, we change “-1” to “-1/2”

For example, we have a multi-classification problem with 10 classes, F will be set to 10. After having the 10-dimension vectors at layer 2, we pass these vectors through a softmax function for the prediction.

The Loss function is simply calculated by the cross-entropy error over all labeled examples, where Y_{l} is the set of node indices that have labels.

The number of layers

The meaning of #layers

The number of layers is the farthest distance that node features can travel. For example, with 1 layer GCN, each node can only get the information from its neighbors. The gathering information process takes place independentlyat the same time for all the nodes.

When stacking another layer on top of the first one, we repeat the gathering info process, but this time, the neighbors already have information about their own neighbors (from the previous step). It makes the number of layers as the maximum number of hops that each node can travel. So, depends on how far we think a node should get information from the networks, we can config a proper number for #layers. But again, in the graph, normally we don’t want to go too far. With 6–7 hops, we almost get the entire graph which makes the aggregation less meaningful.

graph convolutional network
Example: Gathering info process with 2 layers of target node i

How many layers should we stack the GCN?

In the paper, the authors also conducted some experiments with shallow and deep GCNs. From the figure below, we see that the best results are obtained with a 2- or 3-layer model. Besides, with a deep GCN (more than 7 layers), it tends to get bad performances (dashed blue line). One solution is to use the residual connections between hidden layers (purple line).

graph convolutional network
Performance over #layers. Picture from the paper [3]

Take home notes

  • GCNs are used for semi-supervised learning on the graph.
  • GCNs use both node features and the structure for the training
  • The main idea of the GCN is to take the weighted average of all neighbors’ node features (including itself): Lower-degree nodes get larger weights. Then, we pass the resulting feature vectors through a neural network for training.
  • We can stack more layers to make GCNs deeper. Consider residual connections for deep GCNs. Normally, we go for 2 or 3-layer GCN.
  • Maths Note: When seeing a diagonal matrix, think of matrix scaling.
  • A demo for GCN with StellarGraph library here [5]. The library also provides many other algorithms for GNNs.

Note from the authors of the paper: The framework is currently limited to undirected graphs (weighted or unweighted). However, it is possible to handle both directed edges and edge features by representing the original directed graph as an undirected bipartite graph with additional nodes that represent edges in the original graph.

What’s next?

With GCNs, it seems we can make use of both the node features and the structure of the graph. However, what if the edges have different types? Should we treat each relationship differently? How to aggregate neighbors in this case? What are the advanced approaches recently?

In the next post of the graph topic, we will look into some more sophisticated methods.

graph convolutional network
How to deal with different relationships on the edges (brother, friend,….)?

REFERENCES

[1] Excellent slides on Graph Representation Learning by Jure Leskovec (Stanford):  https://drive.google.com/file/d/1By3udbOt10moIcSEgUQ0TR9twQX9Aq0G/view?usp=sharing

[2] Video Graph Convolutional Networks (GCNs) made simple: https://www.youtube.com/watch?v=2KRAOZIULzw

[3] Paper Semi-supervised Classification with Graph Convolutional Networks (2017): https://arxiv.org/pdf/1609.02907.pdf

[4] GCN source code: https://github.com/tkipf/gcn

[5] Demo with StellarGraph library: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/gcn-node-classification.html

This article was originally published on Medium and re-published to TOPBOTS with permission from the author.

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Microsoft BOT Framework — Loops

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Loops is one of the basic programming structure in any programming language. In this article, I would demonstrate Loops within Microsoft BOT framework.

To follow this article clearly, please have a quick read on the basics of the Microsoft BOT framework. I wrote a couple of articles sometime back and the links are below:

Let’s Get Started.

I would be using the example of a TaxiBot described in one of my previous article. The BOT asks some general questions and books a Taxi for the user. In this article, I would be providing an option to the user to choose there preferred cars for the ride. The flow will look like below:

Create a new Dialog Class for Loops

We would need 2 Dialog classes to be able to achieve this task:

  1. SuperTaxiBotDialog.cs: This would be the main dialog class. The waterfall will contains all the steps as defined in the previous article.
  2. ChooseCarDialog.cs: A new dialog class will be created which would allow the user to pick preferred cars. The loop will be defined in this class.

The water fall steps for both the classes could be visualized as:

The complete code base is present on the Github page.

Important Technical Aspects

  • Link between the Dialogs: In the constructor initialization of SuperTaxiBotDialog, add a dialog for ChooseCarDialog by adding the line:
AddDialog(new ChooseCarDialog());

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  • Call ChooseCarDialog from SuperTaxiBotDialog: SuperTaxiBotDialog calls ChooseCarDialog from the step SetPreferredCars, hence the return statement of the step should be like:
await stepContext.BeginDialogAsync(nameof(ChooseCarDialog), null, cancellationToken);
  • Return the flow back from ChooseCarDialog to SuperTaxiBotDialog: Once the user has selected 2 cars, the flow has to be sent back to SuperTaxiBotDialog from the step LoopCarAsync. This should be achieved by ending the ChooseCarDialog in the step LoopCarAsync.
return await stepContext.EndDialogAsync(carsSelected, cancellationToken);

The complete code base is present on the Github page.

Once the project is executed using BOT Framework Emulator, the output would look like:

Hopefully, this article will help the readers in implementing a loop with Microsoft BOT framework. For questions: Hit me.

Regards

Tarun

Source: https://chatbotslife.com/microsoft-bot-framework-loops-fe415f0e7ca1?source=rss—-a49517e4c30b—4

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The Bleeding Edge of Voice

This fall, a little known event is starting to make waves. As COVID dominates the headlines, an event called “Voice Launch” is pulling…

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Tapaan Chauhan

This fall, a little known event is starting to make waves. As COVID dominates the headlines, an event called “Voice Launch” is pulling together an impressive roster of start-ups and voice tech companies intending to uncover the next big ideas and start-ups in voice.

While voice tech has been around for a while, as the accuracy of speech recognition improves, it moves into its prime. “As speech recognition moves from 85% to 95% accuracy, who will use a keyboard anymore?” says Voice Launch organizer Eric Sauve. “And that new, more natural way to interact with our devices will usher in a series of technological advances,” he added.

Voice technology is something that has been dreamt of and worked on for decades all over the world. Why? Well, the answer is very straightforward. Voice recognition allows consumers to multitask by merely speaking to their Google Home, Amazon Alexa, Siri, etc. Digital voice recording works by recording a voice sample of a person’s speech and quickly converting it into written texts using machine language and sophisticated algorithms. Voice input is just the more efficient form of computing, says Mary Meeker in her ‘Annual Internet Trends Report.’ As a matter of fact, according to ComScore, 50% of all searches will be done by voice by 2020, and 30% of searches will be done without even a screen, according to Gartner. As voice becomes a part of things we use every day like our cars, phones, etc. it will become the new “norm.”

The event includes a number of inspiration sessions meant to help start-ups and founders pick the best strategies. Companies presenting here include industry leaders like Google and Amazon and less known hyper-growth voice tech companies like Deepgram and Balto and VCs like OMERS Ventures and Techstars.

But the focus of the event is the voice tech start-ups themselves, and this year’s event has some interesting participants. Start-ups will pitch their ideas, and the audience will vote to select the winners. The event is a cross between a standard pitchfest and Britain’s Got Talent.

Source: https://chatbotslife.com/the-bleeding-edge-of-voice-67538bd859a9?source=rss—-a49517e4c30b—4

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